Journal of Transport and Land Use
https://jtlu.org/index.php/jtlu
<p>The Journal of Transport and Land Use is the leading international journal that publishes original interdisciplinary papers on the interaction of transport and land use. The Editors welcome original submissions across the globe and from a wide range of domains, including engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems.</p>Center for Transportation Studies at the University of Minnesotaen-USJournal of Transport and Land Use1938-7849<p>Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-Noncommercial License 4.0</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</p>The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions
https://jtlu.org/index.php/jtlu/article/view/2434
<p>Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.</p>Yantang ZhangXiaowei Hu
Copyright (c) 2024 Yantang Zhang, Xiaowei Hu
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2024-03-222024-03-2217124126710.5198/jtlu.2024.2434Non-linear effects of built environment factors on mode choice: A tour-based analysis
https://jtlu.org/index.php/jtlu/article/view/2403
<p>Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.</p>Jia FangXiang YanTao TaoChangjie Chen
Copyright (c) 2024 Jia Fang, Xiang Yan, Tao Tao, Changjie Chen
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2024-03-202024-03-2017121523910.5198/jtlu.2024.2403Optimization of the subsidy for university faculty relocation in campus suburbanization
https://jtlu.org/index.php/jtlu/article/view/2341
<p>This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.</p>Zhongzhen YangJionghao LiWenyuan ZhouFeng Lian
Copyright (c) 2024 Zhongzhen Yang, Jionghao Li, Wenyuan Zhou, Feng Lian
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2024-03-182024-03-1817118721410.5198/jtlu.2024.2341If you build it, who will come? Exploring the effects of rapid transit on residential movements in Metro Vancouver
https://jtlu.org/index.php/jtlu/article/view/2364
<p>As cities across the world embrace the benefits of rapid transit technology and invest in the expansion of existing infrastructure or plan for the introduction of new lines, the differences in both benefits and externalities that bus rapid transit (BRT) and rail rapid transit (RRT) bring remain unclear. This study aimed to address that gap and understand whether there was a distinction in impacts on the residential migration of households in different income and residential tenure groups as the result of BRT and RRT projects. This was achieved by exploring the effects of both modes in the same metropolitan region—metro Vancouver. This study used three BRT and three RRT lines that were in service for all or part of the 20 years spanning 1996 through 2016 to assess the rates of in-movement of households by income in Census Tracts (CTs) within 800 meters (½-mile) of a given rapid line. Our analysis suggested that areas adjacent to the Expo-Millennium RRT Corridor saw fewer in-movers between the 2001 Census and the 2016 Census than the areas without rapid transit infrastructure, while the same was true for the CTs affected by BRT lines and that had a larger than average share of new housing while holding everything else (e.g., housing supply) constant. While we did not find evidence to state that the presence of rapid transit infrastructure disproportionately affected any one of the income groups, our analysis suggested that there were more affluent renters moving in along the RRT and BRT lines. At the same time, the share of low-income renters that moved into areas close to rapid transit lines remained relatively stable. This research added a unique perspective to the debate cities and transport agencies have been experiencing with respect to decisions around the investment into different transport technologies and contributed to the argument for the need to carefully plan and provide rapid transit infrastructure together with affordable and diverse housing options.</p>Bogdan KapatsilaJordan D. ReaEmily Grisé
Copyright (c) 2024 Bogdan Kapatsila, Jordan D. Rea, Emily Grisé
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2024-03-112024-03-1117116318510.5198/jtlu.2024.2364Exploring spatial association between residential and commercial urban spaces: A machine learning approach using taxi trajectory data
https://jtlu.org/index.php/jtlu/article/view/1800
<p>Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.</p>Lei ZhouWeiye XiaoChen WangHaoran Wang
Copyright (c) 2024 Lei Zhou, Weiye Xiao, Chen Wang, Haoran Wang
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2024-02-292024-02-2917114316110.5198/jtlu.2024.1800Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction
https://jtlu.org/index.php/jtlu/article/view/2348
<p>Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.</p> <p><audio style="display: none;" controls="controls"></audio></p>Jiahui ZhaoZhibin LiPan LiuMingye Zhang
Copyright (c) 2024 Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang
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2024-02-272024-02-2717111514210.5198/jtlu.2024.2348The built environment and the determination of fault in urban pedestrian crashes: Toward a systems-oriented crash investigation
https://jtlu.org/index.php/jtlu/article/view/2335
<p>This study identifies built environmental factors that influence the determination of fault in urban pedestrian crashes in the United States, with implications for both safety and equity. Using data from Columbus, Ohio, we apply regression modeling, spatial analysis, and case studies, and find pedestrians are more likely to be found at fault on fast, high-volume arterial roads with bus stops. We also observe that better provision of crossings leads to more marked intersection crashes, which are less likely to be blamed on pedestrians. In addition, large differences in both the provision of crossings and fault exist between neighborhoods. We interpret findings through the lenses of the systems-oriented safety approaches Safe Systems and Vision Zero. The conclusion argues that the designation of individual responsibility for crashes preempts collective responsibility, preventing wider adoption of design interventions as well as systemic changes to the processes that determine the built environment of US roadways.</p>Jonathan StilesHarvey Miller
Copyright (c) 2024 Jonathan Stiles, Harvey Miller
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2024-02-092024-02-091719711310.5198/jtlu.2024.2335Will you ride the train? A combined home-work spatial segmentation approach
https://jtlu.org/index.php/jtlu/article/view/2278
<p>While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and work location characteristics on the intent to use a new public transport service. Using data from the 2019 Montreal Mobility Survey (n=1698), this study examines the intent to use the Réseau Express Métropolitain (REM), a light rail under construction in Montreal, for commuting. A segmentation analysis is first conducted to characterize commuters based on their home and work location characteristics, resulting in six distinct home-work clusters. The clusters are then included in an ordered logistic regression modelling the intent to use the REM, along with socio-economic and attitudinal characteristics. Results from a dominance analysis reveal that the clusters are the third most important determinants of the intent to use the REM, even when controlling for individual characteristics. The addition of the clusters leads to a significant improvement of the model (likelihood of -2388.9 improved from -2400.7, p-value < 0,05). All other clusters have a significantly lower probability (between 32 and 51% less likely) of intent to use the REM than the <em>typical commuters </em>(who commute from the suburbs to downtown, often by transit), at a 95% confidence interval. These findings underscore the implications of pursuing radial public-transport networks, illustrating the ability of the proposed approach to identify which groups are likely to benefit from a public-transport project and to propose recommendations anchored in joint home and work location patterns.</p>Vincent Obry-LegrosGeneviève Boisjoly
Copyright (c) 2024 Vincent Obry-Legros, Geneviève Boisjoly
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2024-02-052024-02-05171679610.5198/jtlu.2024.2278Comparing the application of different justice theories in equity analysis of transit projects: A case study of the Lisbon Metro Circular Line
https://jtlu.org/index.php/jtlu/article/view/1895
<p>Although issues of equity and accessibility have already been addressed in transportation, especially with regard to the distribution of costs and benefits, there is no consensus on which concept and metric of fairness would be most appropriate for the evaluation of transportation infrastructure proposals. Normally, a utilitarian perspective is adopted, where issues of unequal distribution of costs and benefits are not the main focus. This paper aims to incorporate the assumptions of other justice theories, namely egalitarianism, communitarianism, and Capability Approach (CA), into the equity assessment of transportation infrastructures, and by doing so, pay closer attention to those who are less advantaged or more open to social exclusion. These theories are critically reviewed considering their contribution to the assessment of equity in terms of transportation infrastructure accessibility impacts. Based on the reviewed theories, accessibility indicators are built and used to assess the equity impacts of the Lisbon Metro expansion project. The findings support the importance of adding other justice perspectives to assessing transportation projects. The CA and Maximax support a need to establish minimum or acceptable distribution standards of accessibility. However, the results from the CA are strongly dependent on the assumptions as to the maximum acceptable travel times.</p>Julianno AmorimJoão de Abreu e Silva
Copyright (c) 2024 Julianno Amorim; João Abreu e Silva
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2024-01-242024-01-24171214010.5198/jtlu.2024.1895Can infrastructure, built environment, and geographic factor negate weather impact on Strava cyclists?
https://jtlu.org/index.php/jtlu/article/view/2318
<p class="AbstracttextTitlePage">Cycling participation is context-sensitive and weather condition is reportedly a significant factor. How weather affects cyclists with different demographics, trip purposes, and in the context of cycling infrastructure, built environment and geographic factors is less well understood by existing literature. This paper applies autoregressive models to explain difference in Strava cycling volume from the same hour of the previous day as a function of change in weather conditions, and day of the week; the contextual effect of cycling infrastructure, built environment and geographic factors is accounted for using interaction terms. We use Strava crowdsourced cycling data in Sydney, Australia, as a case study; commute and leisure cyclists, male and female, young and older cyclists are modeled separately. We find weather conditions have a statistically significant effect on cycling participation; rain, rainfall in the last 2 hours and wind are general deterrents to cycling. Physically separated cycling lanes reduce the adverse effect of precipitation on leisure cyclists and male cyclists but have little effect in retaining commute cyclists and female cyclists. The adverse effect of precipitation and wind on commute cycling is amplified in areas with good access to jobs, possibly due to the availability of better alternative modes of transport. Inland locations generally attenuate effects of windy conditions, except for young adults. This paper sheds light on factors attenuating adverse weather effects on cycling participation and provides useful guidance for future cycling infrastructure.</p>Hao WuSunhyung YooChristopher PettitJinwoo Lee
Copyright (c) 2024 Hao Wu, Sunhyung Yoo, Christopher Pettit, Jinwoo Lee
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2024-01-192024-01-1917112010.5198/jtlu.2024.2318The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review
https://jtlu.org/index.php/jtlu/article/view/2315
<p>To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.</p>Tao TaoGreg LindseyRaphael SternMichael Levin
Copyright (c) 2024 Tao Tao, Greg Lindsey, Raphael Stern, Michael Levin
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2024-02-012024-02-01171416510.5198/jtlu.2024.2315